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Using Machine Learning to Capture Heterogeneity in Trade Agreements

Scott Baier and Narendra R. Regmi
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Narendra R. Regmi: University of WI–Whitewater

Open Economies Review, 2023, vol. 34, issue 4, No 5, 863-894

Abstract: Abstract This paper uses machine learning techniques to capture heterogeneity in free trade agreements. The tools of machine learning allow us to quantify several features of trade agreements, including volume, comprehensiveness, and legal enforceability. Combining machine learning results with gravity analysis of trade, we find that more comprehensive agreements result in larger estimates of the impact of trade agreements. In addition, we identify the policy provisions that have the most substantial effect on creating trade flows. In particular, legally binding provisions on antidumping, capital mobility, competition, customs harmonization, dispute settlement mechanism, e-commerce, environment, export and import restrictions, freedom of transit, investment, investor-state dispute settlement, labor, public procurement, sanitary and phytosanitary measures, services, technical barriers to trade, telecommunications, and transparency tend to have the largest trade creation effects.

Keywords: Free trade agreements; Machine learning; Gravity model; Deep integration (search for similar items in EconPapers)
JEL-codes: F10 F13 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11079-022-09685-3

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